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- Genre: Masters Thesis
- Creators: Turaga, Pavan
Description
As the 3rd generation solar cell, quantum dot solar cells are expected to outperform the first 2 generations with higher efficiency and lower manufacture cost. Currently the main problems for QD cells are the low conversion efficiency and stability. This work is trying to improve the reliability as well as the device performance by inserting an interlayer between the metal cathode and the active layer. Titanium oxide and a novel nitrogen doped titanium oxide were compared and TiOxNy capped device shown a superior performance and stability to TiOx capped one. A unique light anneal effect on the interfacial layer was discovered first time and proved to be the trigger of the enhancement of both device reliability and efficiency. The efficiency was improved by 300% and the device can retain 73.1% of the efficiency with TiOxNy when normal device completely failed after kept for long time. Photoluminescence indicted an increased charge disassociation rate at TiOxNy interface. External quantum efficiency measurement also inferred a significant performance enhancement in TiOxNy capped device, which resulted in a higher photocurrent. X-ray photoelectron spectrometry was performed to explain the impact of light doping on optical band gap. Atomic force microscopy illustrated the effect of light anneal on quantum dot polymer surface. The particle size is increased and the surface composition is changed after irradiation. The mechanism for performance improvement via a TiOx based interlayer was discussed based on a trap filling model. Then Tunneling AFM was performed to further confirm the reliability of interlayer capped organic photovoltaic devices. As a powerful tool based on SPM technique, tunneling AFM was able to explain the reason for low efficiency in non-capped inverted organic photovoltaic devices. The local injection properties as well as the correspondent topography were compared in organic solar cells with or without TiOx interlayer. The current-voltage characteristics were also tested at a single interested point. A severe short-circuit was discovered in non capped devices and a slight reverse bias leakage current was also revealed in TiOx capped device though tunneling AFM results. The failure reason for low stability in normal devices was also discussed comparing to capped devices.
ContributorsYu, Jialin (Author) / Jabbour, Ghassan E. (Thesis advisor) / Alford, Terry L. (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Semiconductor nanowires are featured by their unique one-dimensional structure which makes them promising for small scale electronic and photonic device applications. Among them, III-V material nanowires are particularly outstanding due to their good electronic properties. In bulk, these materials reveal electron mobility much higher than conventional silicon based devices, for example at room temperature, InAs field effect transistor (FET) has electron mobility of 40,000 cm2/Vs more than 10 times of Si FET. This makes such materials promising for high speed nanowire FETs. With small bandgap, such as 0.354 eV for InAs and 1.52 eV for GaAs, it does not need high voltage to turn on such devices which leads to low power consumption devices. Another feature of direct bandgap allows their applications of optoelectronic devices such as avalanche photodiodes. However, there are challenges to face up. Due to their large surface to volume ratio, nanowire devices typically are strongly affected by the surface states. Although nanowires can be grown into single crystal structure, people observe crystal defects along the wires which can significantly affect the performance of devices. In this work, FETs made of two types of III-V nanowire, GaAs and InAs, are demonstrated. These nanowires are grown by catalyst-free MOCVD growth method. Vertically nanowires are transferred onto patterned substrates for coordinate calibration. Then electrodes are defined by e-beam lithography followed by deposition of contact metals. Prior to metal deposition, however, the substrates are dipped in ammonium hydroxide solution to remove native oxide layer formed on nanowire surface. Current vs. source-drain voltage with different gate bias are measured at room temperature. GaAs nanowire FETs show photo response while InAs nanowire FETs do not show that. Surface passivation is performed on GaAs FETs by using ammonium surfide solution. The best results on current increase is observed with around 20-30 minutes chemical treatment time. Gate response measurements are performed at room temperature, from which field effect mobility as high as 1490 cm2/Vs is extracted for InAs FETs. One major contributor for this is stacking faults defect existing along nanowires. For InAs FETs, thermal excitations observed from temperature dependent results which leads us to investigate potential barriers.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Ferry, David (Committee member) / Tracy, Clarence (Committee member) / Arizona State University (Publisher)
Created2011
Description
Semiconductor devices are generally analyzed with relatively simple equations or with detailed computer simulations. Most text-books use these simple equations and show device diagrams that are frequently very simplified and occasionally incorrect. For example, the carrier densities near the pinch-off point in MOSFETs and JFETs and the minority carrier density in the base near the reverse-biased base-collector junction are frequently assumed to be zero or near zero. Also the channel thickness at the pinch-off point is often shown to approach zero. None of these assumptions can be correct. The research in thesis addresses these points. I simulated the carrier densities, potentials, electric fields etc. of MOSFETs, BJTs and JFETs at and near the pinch-off regions to determine exactly what happens there. I also simulated the behavior of the quasi-Fermi levels. For MOSFETs, the channel thickness expands slightly before the pinch-off point and then spreads out quickly in a triangular shape and the space-charge region under the channel actually shrinks as the potential increases from source to drain. For BJTs, with collector-base junction reverse biased, most minority carriers diffuse through the base from emitter to collector very fast, but the minority carrier concentration at the collector-base space-charge region is not zero. For JFETs, the boundaries of the space-charge region are difficult to determine, the channel does not disappear after pinch off, the shape of channel is always tapered, and the carrier concentration in the channel decreases progressively. After simulating traditional sized devices, I also simulated typical nano-scaled devices and show that they behave similarly to large devices. These simulation results provide a more complete understanding of device physics and device operation in those regions usually not addressed in semiconductor device physics books.
ContributorsYang, Xuan (Author) / Schroder, Dieter K. (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
ContributorsRajan, Deepta (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Fluxgate sensors are magnetic field sensors that can measure DC and low frequency AC magnetic fields. They can measure much lower magnetic fields than other magnetic sensors like Hall effect sensors, magnetoresistive sensors etc. They also have high linearity, high sensitivity and low noise. The major application of fluxgate sensors is in magnetometers for the measurement of earth's magnetic field. Magnetometers are used in navigation systems and electronic compasses. Fluxgate sensors can also be used to measure high DC currents. Integrated micro-fluxgate sensors have been developed in recent years. These sensors have much lower power consumption and area compared to their PCB counterparts. The output voltage of micro-fluxgate sensors is very low which makes the analog front end more complex and results in an increase in power consumption of the system. In this thesis a new analog front-end circuit for micro-fluxgate sensors is developed. This analog front-end circuit uses charge pump based excitation circuit and phase delay based read-out chain. With these two features the power consumption of analog front-end is reduced. The output is digital and it is immune to amplitude noise at the output of the sensor. Digital output is produced without using an ADC. A SPICE model of micro-fluxgate sensor is used to verify the operation of the analog front-end and the simulation results show very good linearity.
ContributorsPappu, Karthik (Author) / Bakkaloglu, Bertan (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2013
Description
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013